Complex Network Classification with Convolutional Neural Network
This work addresses the problem of network classification for applications in real-life domains like trade analysis, but it is incremental as it combines existing techniques (network embedding and CNNs) for a known bottleneck.
The paper tackles the problem of classifying large-scale complex networks, which is challenging due to their non-Euclidean properties, by proposing a novel framework called CNC that integrates network embedding and convolutional neural networks. The result shows that CNC achieves high accuracy and robustness in classifying synthetic and real-world international trade networks, while automatically extracting network features.
Classifying large scale networks into several categories and distinguishing them according to their fine structures is of great importance with several applications in real life. However, most studies of complex networks focus on properties of a single network but seldom on classification, clustering, and comparison between different networks, in which the network is treated as a whole. Due to the non-Euclidean properties of the data, conventional methods can hardly be applied on networks directly. In this paper, we propose a novel framework of complex network classifier (CNC) by integrating network embedding and convolutional neural network to tackle the problem of network classification. By training the classifiers on synthetic complex network data and real international trade network data, we show CNC can not only classify networks in a high accuracy and robustness, it can also extract the features of the networks automatically.